Sas Institute Inc.

Manoj Keshavmurthi Chari

Robert William Pratt

Joshua David Griffin

Yan Xu

Jun Liu

Susan Edwards Haller

Ivan Borges Oliveira

Nicholas Akbar Ablitt

Yi Liao

Balaji Gopalakrishnan

David Rawlins Duling

Jack Joseph Rouse

James Byron Morris

Jared Carl Erickson

Michelle Gail Opp

A computing system displays an initial graph with icons. Each icon graphically represents data associated with a respective entity. The first icon is connected in the initial graph to other icon(s). The system receives an indication of a graphical network pattern. The graphical network pattern is defined by a user selection of a second icon in the initial graph and: a user selection of a third icon in the initial graph; or a user selection of a graphical representation in the initial graph of a relationship between the second icon and the third icon. The system sends computer instructions indicating a network pattern query for searching an electronic database for electronic record(s) corresponding to a queried network pattern. The system receives a dataset indicating located electronic record(s) corresponding to the queried network pattern. The system generates output data indicating an output graph for a graphical representation of the located record(s).

Graphical user interface for searching on a network pattern

A processor core receives a request to execute application code including a trigger instruction and an instruction block that reads a row of data values from a data structure and outputs a data value from a function using the row as input. The data structure is divided into multiple portions and the trigger instruction indicates that multiple instances of the instruction block are to be executed concurrently. In response to the request and to identification of the instruction block and trigger instruction, the processor core generates multiple instances of a support block that causes independent repetitive execution of each instance of the instruction block until all rows of the corresponding portion of the data structure are used as input. The processor core assigns instances of the instruction and support blocks to multiple processor cores, and provides each instance of the instruction block with the corresponding portion of the data structure.

Automated concurrency and repetition with minimal syntax

A computing device trains models for streaming classification. A baseline penalty value is computed that is inversely proportional to a square of a maximum explanatory variable value. A set of penalty values is computed based on the baseline penalty value. For each penalty value of the set of penalty values, a classification type model is trained using the respective penalty value and the observation vectors to compute parameters that define a trained model, the classification type model is validated using the respective penalty value and the observation vectors to compute a validation criterion value that quantifies a validation error, and the validation criterion value, the respective penalty value, and the parameters that define a trained model are stored to the computer-readable medium. The classification type model is trained to predict the response variable value of each observation vector based on the respective explanatory variable value of each observation vector.

Classification system training

Computer-implemented systems and methods are provided for determining an action item from a global set of action items for a plurality of customers based on an objective function, a plurality of individual constraints, and a plurality of aggregate constraints. A plurality of offer sets is generated for each customer. An approximate highest reduced adjusted objective for each of the offer sets for each customer is calculated, and the customers are bucketed based on the highest adjusted objective value associated with each customer. The buckets are collapsed into a single bucket record containing a plurality of aggregate offer set columns, and an aggregate offer set column is selected from each bucket record for each bucket. Each bucket associated with a selected offer set is disaggregated, and the action item included in the selected offer set is stored in a computer-readable memory.

Computer-implemented marketing optimization systems and methods

Computer-implemented systems and methods are provided for generating bins for a scorecard. An approximate set of bins is generated by applying an optimization model to binning data. The optimization model includes an objective function, constraints, and surrogate weight of evidence metric(s). The approximated set of bins are then used in scorecard operations.

Constrained optimized binning for scorecards

Growing community of inventors

Cary, NC, United States of America

Manoj Keshavmurthi Chari